19099 results about "Data analysis" patented technology

The present invention relates to the technical field of monitoring, in particular to a remote monitoring method and system for an aviation obstruction light. The method comprises the following steps: on the basis of an external sensor, acquiring electromagnetic signals sent by an aviation obstruction light; and by means of using a signal processing algorithm, processing the obtained original signals to eliminate noise interference and standardize the signal format, so as to generate signal-purified data. Using a support vector machine and a random forest algorithm in the present invention enhances the fault mode identification capability and the accuracy of predicting device performance degradation trends, and substantially improves the reliability of fault prediction; the combination of a Kalman filter and a multi-level decision tree provides powerful support for the integration and analysis of multi-source data, thereby ensuring the comprehensiveness and effectiveness of decision-making support information; and using a self-organizing map network and U matrix visualization technology not only shows advantages in the aspects of data mode identification and anomaly detection, but also improves the interpretability of data analysis by means of visual image displaying.

The invention relates to a network public opinion intelligent classification and emergency decision-making system based on multi-modal fusion and dynamic evolution, and belongs to the field of network public opinion monitoring and big data analysis and artificial intelligence. The system comprises a multi-source data acquisition and preprocessing module used for crawling multi-modal data, constructing a propagation path map after preprocessing, and identifying key propagation nodes; the multi-dimensional classification engine module is used for carrying out conflict intensity quantification on public opinion events and dynamically updating a rule word bank to keep the adaptability of a conflict intensity quantification model; the event graph construction and anomaly detection module is used for constructing a public opinion propagation path and public opinion event generality logic chain mode, monitoring public opinion propagation speed and giving an alarm; the stakeholder dynamic risk assessment module is used for finely classifying network public opinion participants, providing a basis for differential propagation intervention and simulating public opinion evolution to carry out risk simulation; and the intelligent decision-making and emergency response module executes different levels of emergency measures based on the risk index according to the hierarchical response strategy.

The invention relates to the technical field of data analysis, in particular to an operation decision intelligent analysis method and system based on cross-domain data fusion. The method comprises the following steps: firstly, based on an enterprise multi-domain ontology knowledge base, performing entity identification and relation mapping on heterogeneous data from different business systems through a semantic mapping-based multi-source heterogeneous data dynamic fusion algorithm, and establishing a unified data model; then, a causal reasoning and deep learning fused hybrid intelligent decision engine is adopted to analyze and process the model; then, a multi-level causal relationship network among business variables is constructed through a causal relationship discovery algorithm by utilizing an analysis result of the hybrid intelligent decision engine, and an adaptive business scene analysis model based on reinforcement learning is used to dynamically adjust an analysis strategy according to business environment changes; generating a Pareto optimal decision scheme set through a multi-objective optimization algorithm, and outputting operation decision suggestions; according to the invention, the comprehensiveness and accuracy of intelligent analysis of enterprise operation decisions are improved.

The invention relates to the field of electric power monitoring, and discloses an electric power monitoring system and method fusing image recognition and data analysis, and the method comprises the steps: carrying out the visual angle coverage modeling of a target equipment group through a multi-type visual collection unit disposed at a transformer substation and a power distribution terminal; performing cross-frame fine-grained texture differential analysis on the equipment state image sequence, and constructing an image event time window in combination with synchronous disturbance characteristics of multi-source monitoring parameters; based on the high-vigilance candidate frame set, fusing the image structure variability index and the operation data multi-dimensional deviation vector by using a feature encoder, and constructing a multi-modal state coupling feature tensor; map mapping is carried out on the potential fault evolution trend, and semantic association is established between structural nodes with abnormal attributes in the image and frequently fluctuating parameter indexes in the monitoring data; and combining a node interference path in the local fault association subgraph with fault precursor distribution induced in a historical accident sample. The method has the advantage that the operation safety is improved.

The present invention relates to the technical field of power system equipment and control. The method of the present invention comprises: installing sensors to acquire data for online real-time monitoring, and constructing a comprehensive condition online monitoring model for comprehensive data analysis; comprehensively evaluating the health condition of a generator on the basis of a comprehensive analysis result, and identifying fault causes; and constructing a generator comprehensive data intelligent monitoring and dynamic early warning model to perform insulation degradation trend analysis and prediction on the generator. In the present invention, by comprehensively monitoring the condition of a turbine generator, various key indicators of the generator are captured in real time, and trends and patterns underlying the data are revealed, providing technical support for accurately evaluating the health condition of the generator; and continuous monitoring for the insulation condition of the generator allows for proactive identification of potential risks, thereby providing decision-making support for preventive maintenance, helping operators take measures promptly, preventing faults, improving the reliability and safety of the generator.

The invention discloses a multi-modal interview automatic quality analysis and evaluation method and system based on a large model, and the method comprises the steps: collecting and storing multi-modal data, such as texts, audios, videos and behavior interaction, and carrying out the preprocessing of the multi-modal data to form a standardized data set; utilizing a preset interview structure and a large model to dynamically guide the process, adjusting the topic rhythm according to real-time feedback, and recording stage conversion information to form logic trajectory data for process coherence management; automatically coding text data through a large language model, extracting features such as keywords and performing topic clustering, performing cross validation and semantic fusion in combination with data analysis results of each modal, and generating deep analysis results such as psychological states; and generating a comprehensive assessment report containing qualitative description, quantitative score and psychological abnormality or cognitive disorder risk prompts based on a deep analysis result, thereby providing a basis for psychological health assessment and cognitive competence evaluation. According to the method, automatic analysis of multi-modal data is realized, and evaluation scientificity and efficiency are improved.

The invention discloses a grassroots power supply enterprise compliance risk intelligent system and method based on big data analysis. The data acquisition unit is used for acquiring business operation data, historical violation records and policy and regulation update data of basic power supply enterprises to form a unified compliance data set. And the natural language processing unit performs text word segmentation and correlation analysis on the policy and regulation and violation record data, extracts key risk factors and labels compliance risk labels. And the risk feature construction unit performs multi-dimensional feature fusion on the business operation data and the compliance risk label data to generate a feature matrix for risk identification. And the intelligent risk assessment unit performs real-time analysis on the feature matrix by using a pre-trained machine learning model, identifies compliance risk categories and levels, and generates early warning information. According to the method, the accuracy of compliance risk identification is improved by using big data analysis and an intelligent algorithm, the compliance management cost of basic-level power supply enterprises is reduced, and the operation safety and compliance of the enterprises are improved.

The invention relates to the technical field of factory energy consumption management, in particular to a smart factory dynamic optimization management system based on digital twinning and big data analysis. Comprising a data acquisition and fusion module, a digital twinning construction module, a data analysis module, a dynamic optimization decision module and an anomaly diagnosis module. Constructing a digital twinborn model of a factory physical entity according to the collected data; constructing an energy consumption prediction model based on deep learning frameworks such as LTSM; when the energy consumption deviation exceeds the limit, abnormal root causes are positioned; and generating an energy consumption scheduling scheme based on a multi-objective optimization algorithm, and issuing an instruction to realize dynamic energy consumption adjustment. Through deep fusion of digital twinning and big data technologies, comprehensive and accurate simulation, multi-target collaborative optimization, rapid abnormality diagnosis and dynamic control of factory energy consumption are realized, the energy utilization efficiency is effectively improved, the cost is reduced, the intelligent level is improved, and the method has remarkable economic benefits and environmental benefits.

The invention relates to the technical field of communication data analysis, and discloses a communication network service health degree analysis method, system and device and a medium, and the method comprises the steps: obtaining real-time link state data, carrying out the quantitative analysis, and judging whether the link jitter exceeds a preset threshold value or not; when abnormality is detected, abnormal points can be positioned and the influence range of the abnormal points can be analyzed, so that the influence degree of nodes on the abnormal diffusion path can be identified; calculating a service quality index change value, and determining a starting point and a diffusion direction of chain degradation; by analyzing historical traffic data of the key nodes, a service traffic change trend and traffic dense area distribution characteristics are obtained. According to the method, the bandwidth allocation strategy is dynamically optimized, the bandwidth resources at the junction are reconfigured, and the regional service health degree is evaluated. And if the degradation is not effectively controlled, monitoring and detection mechanism parameters can be adaptively adjusted, and continuous analysis and control of link abnormity are realized, so that the network performance and the service quality are effectively improved.

The invention specifically relates to the technical field of big data analysis, and discloses a water conservancy gate multi-parameter cooperative intelligent monitoring system, which comprises a multi-parameter acquisition module, a multi-parameter processing module, a comprehensive analysis module, an intelligent decision module, an operation and maintenance early warning module and a man-machine interaction module, the multi-parameter processing module is used for calculating flood control and discharge indexes, structure safety indexes and equipment health indexes; the comprehensive analysis module is used for judging gate risk levels; the intelligent decision-making module is used for generating an optimal gate scheduling scheme; the operation and maintenance early warning module is used for constructing a multi-level early warning mechanism; according to the method, parameter coverage is comprehensive, a gate digital twinborn model and a gate opening comprehensive evaluation model are constructed, the gate risk level is evaluated, an optimization strategy is dynamically adjusted through an intelligent decision module, the accuracy of gate risk judgment is improved, and the self-adaptive capacity of the system is improved.

The invention relates to the technical field of exception prediction, in particular to a storage cabinet exception trend prediction system based on time series data analysis, which comprises a state monitoring module, an interval sensing module, a path reconstruction module, a symptom activation module and an evolution prediction module. According to the method, the state vectors including the temperature, the voltage, the current and the door lock state are constructed and combined with the timestamp information to form the time sequence data sequence, and the dynamic expression mode of state change is established; a jump characteristic is analyzed by using a ratio of a time interval to a state change amplitude, a short-time disturbance path and a trend evolution path are distinguished by combining a jump rate statistical index, and an evolution activation signal is identified based on trend maintenance and non-fallback characteristics. On the basis, a neural network structure with long-time dependent learning ability is introduced to capture an aperiodic thermal anomaly trend in a state sequence, and the accuracy and timeliness of anomaly recognition are improved through multi-dimensional parameter cooperative processing and path construction logic.

The invention relates to the technical field of risk analysis, in particular to a dangerous chemical transportation risk prediction system and method based on big data analysis, and the system comprises a multi-dimensional data acquisition module which is used for collecting human-machine-ring-pipe four-dimensional data in the liquid ammonia water transportation process, and carrying out the standardization processing and time-space synchronization, and obtaining a multi-source heterogeneous data set; the risk level acquisition module is used for constructing a man-machine-ring-management collaborative risk assessment model and analyzing the multi-source heterogeneous data set to obtain a risk assessment result; the human-machine-environment-management collaborative risk assessment model comprises a human-machine interaction key node identification layer, a management behavior-equipment response association analysis layer, an environment-material interaction dynamic risk assessment layer and a multi-dimensional factor risk cascade assessment layer; and the emergency disposal scheme acquisition module constructs an emergency disposal scheme intelligent recommendation model to perform grading and classification analysis on the risk assessment result, generates a multi-level risk early warning and emergency disposal scheme, and optimizes the emergency disposal scheme.

The invention relates to the technical field of energy scheduling, and discloses a smart city energy dynamic scheduling system and method based on big data analysis, and the method comprises the following steps: the operation state of a transformer substation, the basic parameters of a charging pile and regional load prediction data are collected in real time, data cleaning and abnormal value filtering are carried out; constructing a complete power grid-charging facility dynamic information base; calculating the power supply margin of each region based on the capacity loss of the faulty transformer substation, establishing a weight scoring system of charging pile power adjustment in combination with the charging demand urgency, and determining the reduction or recovery priority of each charging load; and generating a charging pile power adjustment instruction through a multi-target optimization model, and iteratively correcting a power distribution scheme and generating a final scheduling instruction set by taking minimization of user satisfaction loss as a target while meeting the power grid capacity. According to the invention, by constructing a dynamic response mechanism and a multi-target collaborative optimization model, accurate and rapid regulation and control of the traffic load are realized in the scene of sudden power shortage of the power grid.

The invention is suitable for the technical field of data analysis, and provides a transformer substation fault handling method combining causal reasoning knowledge graph modeling, comprising: acquiring multi-source heterogeneous data and performing data cleaning processing to obtain a space-time alignment data set, the space-time alignment data set comprising one or more quaternary data sets, the quaternary data set comprises a device identifier, a timestamp, a feature vector and an event tag; causal modeling processing is carried out on the time-space alignment data set to obtain a causal graph, and the causal graph comprises node information of nodes and relation information between the nodes; constructing a space-time diagram neural network model according to the causal diagram and the equipment connection relation diagram, wherein the space-time diagram neural network model realizes dynamic evolution of the graph based on an incremental updating strategy; and outputting fault root cause positioning information according to the time-space diagram neural network model.

The invention belongs to the field of data analysis, and discloses a data analysis method for engineering consultation digital intelligent management, which comprises the following steps: mapping multi-project heterogeneous data to a unified three-dimensional space-time grid through space-time grid division to form a grid mapping data set; detecting data format and attribute conflicts based on neighborhood correlation analysis, and distributing calibration data through dynamic weight; missing values and abnormal fluctuations are processed by using a time sequence autoregression model and a smoothing technology, and a complete data set with continuous time is constructed by combining linear interpolation and multi-dimensional data fusion; key indexes are extracted to construct a digital mirror image containing spatial positions, time sequences and attribute association, real-time data are synchronized, and outlier grid points are corrected; and multi-scene operation conditions are constructed based on the mirror image data, and a dynamic adjustment scheme is realized. According to the method, heterogeneous data are integrated through space-time grids, digital twinning and multi-scene simulation technologies are combined, and full-period management and risk intelligent decision-making of engineering data are achieved.

The invention discloses a coal mine power supply intelligent monitoring system based on the Internet of Things, belongs to the field of coal mine power supply monitoring, and aims to solve the problems that an existing coal mine power supply intelligent monitoring system is lagged in response, high in false alarm rate and large in manual dependence degree. According to the invention, through the end-side global sensing module, the data advanced analysis module, the edge data processing module, the data transmission module, the cloud data analysis and model construction module and the fault early warning and closed-loop control module, the real-time acquisition of the equipment state is realized by deploying multiple types of intelligent sensors; local data preprocessing and abnormal pre-judgment are carried out by combining edge computing nodes, an equipment health degree model is established by adopting a time sequence data association analysis algorithm, closed-loop control of overload prediction, electric leakage positioning and energy consumption optimization is realized through multi-source data fusion analysis, and finally a three-level intelligent monitoring system of end side sensing-edge computing-cloud decision is formed. The system response efficiency and accuracy are improved, and the personal labor intensity is reduced.

The invention relates to the technical field of artificial intelligence and big data analysis, in particular to a data operation system and method based on a knowledge graph, and the method comprises the steps: extracting an entity and semantic relationship from multi-source business data, and constructing a dynamic evolvable initial knowledge graph; node features are aggregated, and multi-dimensional situation state vectors are generated in combination with gating loop unit modeling behavior path dependence; through a structure-semantic coupling attribution scoring mechanism, a statistical information gain and semantic similarity are fused to identify a core driving factor, and a causal regression model of the factor and an operation target is established; dynamically adjusting the edge weight and the structure of the atlas in real time, and triggering a new path discovery mechanism to continuously optimize the atlas; and according to a quantitative business target, reversely extracting a high-confidence influence path from the atlas, and generating a personalized strategy combination through intervention simulation and multi-target Pareto optimization, thereby realizing intelligent recommendation and decision closed loop driven by an operation target. According to the invention, higher-precision operation situation awareness and strategy generation are realized.

The invention relates to the technical field of data analysis, and discloses a GPU computing power resource scheduling method and system, and the method comprises the steps: collecting node hardware parameters and dynamic load indexes of a GPU cluster to construct a multi-dimensional resource feature vector of the GPU cluster, and constructing a resource portrait of the GPU cluster; establishing a node health degree scoring model of the GPU cluster, and generating a health degree score of a cluster node corresponding to the GPU cluster; analyzing a video memory demand of the GPU task request, and calculating an intensive identifier and a communication dependency relationship; determining the SLA weight of the GPU task request, calculating the resource shortage sensitivity of the GPU task request based on the video memory demand, and calculating the target task priority of the GPU task request in combination with the SLA weight; and determining a resource scheduling node group requested by the GPU task in the resource portrait, generating resource scheduling parameters of the resource scheduling node group, and executing scheduling of computing power resources of the GPU cluster based on the resource scheduling parameters. According to the method, the scheduling efficiency of the GPU computing power resources can be improved.

The invention provides a building construction scheme intelligent auditing system based on a large model, and belongs to the field of building construction. According to the technical scheme, the method comprises the steps that a construction scheme template library and a building construction standard specification library are constructed, a building specification knowledge graph is constructed, a to-be-audited construction scheme document is received and subjected to structural division and preprocessing, semantic vector data are obtained, matching operation is conducted on the semantic vector data and graph nodes in the building specification knowledge graph, and the graph nodes in the building specification knowledge graph are obtained; and outputting audit target data, analyzing and generating an analysis result data structure based on the audit target data, generating an audit report according to an analysis result, and completing recording and tracking of an audit process. The method has the beneficial effects that automatic auditing and whole-process tracking of the construction scheme are realized by constructing the structured template and the standard specification knowledge graph in combination with semantic vector matching and rule work efficiency comparison, the accuracy, consistency and efficiency of auditing are effectively improved, and the informatization and intelligence level of building construction management is remarkably enhanced.

The invention relates to the technical field of data analysis, in particular to a new energy power generation equipment health management platform based on a large model, which comprises a data acquisition and perception layer, an edge computing layer, a cloud processing layer and an application service layer, compared with the prior art that static historical data or single equipment parameters are adopted as a health detection reference, and the influence of environment dynamic change and equipment aging cannot be reflected, the scheme adopts a multi-dimensional simulation modeling technology, equipment parameters, weather parameters and other real-time working condition data are integrated to construct a digital twinborn model, and the digital twinborn model can be used for real-time health detection. Dynamic health reference values including generating capacity, instantaneous current / voltage, equipment temperature and the like are generated by simulating equipment operation states (such as photovoltaic efficiency attenuation at an extreme temperature and aerodynamic load of a fan in a salt mist environment) in different scenes. The method can accurately capture the interaction effect of the environment and the equipment, enables the health detection threshold to be dynamically adjusted along with the working condition, and improves the anomaly recognition accuracy by more than 35% compared with a traditional method.

The invention provides an information transmission device and implementation method based on multi-protocol dynamic conversion and data fusion, and belongs to the technical field of information transmission, and the device comprises a multi-protocol communication interface module, a data analysis unit, a dynamic protocol conversion engine, a data fusion unit, a data assembly reporting unit and a log storage module. According to the method, information transmission is realized by initializing an interface, analyzing multi-protocol original data, dynamically converting the multi-protocol original data into a uniform format, fusing de-duplicated data, packaging, reporting and recording logs. The device and the method solve information interaction obstacles of different protocol devices, support bidirectional conversion of multiple industrial protocols, realize cross-protocol device collaboration and centralized management, improve the system intelligence level, and are suitable for scenes requiring multi-device collaboration, such as electric power, intelligent transportation, intelligent buildings and the like.

The embodiment of the invention discloses a multi-agent collaborative data visualization analysis method and device and a medium, and relates to the technical field of visualization analys.The method comprises the steps that a natural language analysis request input by a user is received, task splitting is conducted on the natural language analysis request through a preset task planning agent, and the task splitting result is obtained; generating corresponding task plan information, obtaining real-time operation data of a preset functional agent cluster, carrying out agent distribution on the plurality of sub-tasks based on the real-time operation data and the task plan information, determining a target functional agent corresponding to each sub-task, and sending the target functional agent to a server; the target function agent comprises any one of a data analysis agent and a visual display agent; and executing the corresponding data analysis subtask through the data analysis agent to obtain a data analysis result, performing agent cooperative verification on the data analysis result, and after the verification is passed, generating visual analysis data corresponding to the data analysis result through the visual display agent.

The invention provides a smart home management method and system based on the Internet of Things. The method comprises the steps that indoor and outdoor environment parameters, user physiological data, home equipment operation states and energy consumption data are collected; based on the data, learning behavior preferences of the user in different time and environments, and establishing a personalized behavior prediction model; deploying the model at an edge computing node, combining big data analysis of a cloud server to form a hierarchical intelligent decision-making architecture, and outputting a preliminary decision-making result; the real intention of the user is understood and an intention confidence evaluation mechanism is established; when the consistency of the identification results of the multiple modes is lower than a threshold value, confirmation is actively carried out on the user, and a primary intention is obtained; according to the intention, a control strategy is dynamically generated in combination with the environment state and the equipment capacity; and based on the operation data and the energy consumption data, establishing an equipment health degree evaluation model, and predicting a fault risk and a maintenance demand. Through the scheme of the invention, the intention of the user can be accurately identified, and accurate personalized services are provided, so that the user experience is improved.

The invention relates to the technical field of medical intelligent image recognition data processing, and discloses an image recognition and data analysis fused ultrasonic venipuncture system which comprises a multi-modal image acquisition module, an intelligent analysis processing module, a real-time navigation execution module and a complication early warning module. By arranging a multi-mode fusion sensing end, when vein puncture real-time navigation is carried out, through ultrasonic image, thermodynamic distribution and optical characteristic three-mode data collaborative registration, the consistency of deep blood vessel recognition is guaranteed, meanwhile, a three-dimensional topological model containing blood vessel elastic parameters is dynamically constructed, blood vessel position deviation can be calibrated in real time in the puncture process, and the accuracy of vein puncture is improved. The accuracy of blood vessel positioning is guaranteed, puncture positioning errors of complex cases are further reduced, whether angle or depth deviation occurs in a puncture path or not is judged in real time by arranging a dynamic navigation end, a compensation path can be planned in real time through a blood vessel elastic characteristic matrix, and it is guaranteed that the angle errors are reduced when a needle body enters a blood vessel.

The invention discloses a building engineering quality monitoring system, and relates to the technical field of building engineering monitoring, the building engineering quality monitoring system comprises a collection module, an analysis module, a monitoring module and an early warning module, the collection module collects first quality monitoring data of building engineering and obtains historical monitoring data, and transmits the data to the analysis module; performing feature extraction and data analysis on the first quality monitoring data to obtain second quality monitoring data, storing historical monitoring data and presetting quality standard data, transmitting the second quality monitoring data and the quality standard data to a monitoring module, performing dynamic comparison on the real-time second quality monitoring data and the preset quality standard data, and outputting the result. The method comprises the steps of generating quality anomaly feature parameters, transmitting the quality anomaly feature parameters to an early warning module, matching a preset early warning strategy according to the quality anomaly feature parameters, sending out graded early warning signals, and carrying out multi-dimensional early warning by integrating multi-dimensional data acquisition and analysis, dynamic feature index extraction and dynamic standard construction, so that the engineering quality monitoring accuracy and the management and control timeliness are improved.

The invention relates to the technical field of agricultural intelligent irrigation, and discloses a high-standard farmland intelligent irrigation system based on Internet of Things and data analysis, and the system comprises a soil moisture content sensing module which collects data through a multi-source sensor to construct a three-dimensional soil moisture content distribution model, and generates a soil moisture content characteristic spectrum; the soil moisture content prediction module generates water demand prediction data based on the soil moisture content characteristic spectrum, the meteorological data and the crop growth stage; the irrigation strategy module fuses terrain elevation and pipe network pressure parameters to generate an irrigation control map; the equipment state monitoring module collects water pump current waveform and other data to generate equipment health degree parameters; the pipe network optimization module optimizes pipe network topology and generates an adjusting instruction; the multi-source data fusion module generates fusion evaluation indexes by using an evidence theory, an entropy weight method and the like; and the intelligent execution module generates an execution control instruction accordingly. All the modules cooperate to achieve precise irrigation, and the utilization efficiency of water resources and the intelligent level of farmland management are improved.

According to the artificial intelligence-based data analysis system and method provided by the invention, a set of novel data analysis system architecture is designed, and key technical components such as natural language processing, a large language model, database query optimization, multi-modal visualization and a semantic knowledge graph are fused; data transmission between modules is realized through unified intermediate data objects such as a structured query intention, an analytic tree and a structured query language template object, and asynchronous collaboration is realized through event driving and a message queue mechanism, so that a non-professional user can input an analysis request through a natural language; semantic analysis, structured query language query statement generation, data query and result visualization presentation are automatically completed, the method can be widely applied to data analysis scenes in the industries of government affairs, traffic, finance, education and the like, the data use efficiency is improved, the technical threshold is reduced, and the digital decision-making ability is enhanced.

The invention discloses a wind turbine generator data analysis and fault diagnosis method and system based on big data and artificial intelligence. According to the method, a blade image, a vibration signal, audio data and operation parameters are synchronously acquired through an unmanned aerial vehicle multi-mode sensor and a ground monitoring system, and a multi-source heterogeneous data set is constructed; after the data is classified and preprocessed, image features, vibration time-frequency domain features and operation parameter key value pairs are extracted respectively; dimensionality reduction is carried out by using an auto-encoder, feature-level space-time alignment is realized through an improved DTW algorithm, and a multi-dimensional fault feature matrix is generated; a hierarchical diagnosis model including a GRU auto-encoder, an MLP network and an attention mechanism CNN is constructed, and training is carried out by taking minimization of sub-model deviation as an optimization target; and finally, fusing multi-source features to realize fault classification, and generating a visual diagnosis report. According to the method, efficient fusion and accurate diagnosis of multi-source heterogeneous data are realized, and the accuracy and the real-time performance of fault detection of the wind turbine generator are remarkably improved.

The invention relates to the technical field of text data analysis, in particular to an electronic medical record free text analysis method, system and device, which improve the efficiency of information extraction and reduce the demand of manual intervention. The method comprises the steps of receiving free text data of the electronic medical record, performing cleaning, word segmentation and medical term standardization processing, and converting an unstructured text into structured data; based on deep learning and a medical knowledge base, text features are extracted through a pre-training language model, entity boundaries are captured on an output layer in combination with a conditional random field, medical entities in a text are recognized, and the recognized entities are classified and labeled; extracting a causal relationship, a treatment relationship and an examination relationship among entities through a dependency syntactic analysis and semantic role labeling technology, and constructing an entity association network; performing dynamic correction and supplementation on entity classification and relationships in combination with medical record context information; and outputting an analysis result in a structured JSON format to generate a computable semantic map.

A framework for machine learning modeling of structured data that includes one or more artificial intelligence-based agents. These artificial intelligence-based agents are configured to create and execute chains of repeatable actions to perform user-driven and user-defined workflows with a given problem set and identified outcomes. Structured data that has been processed is fed by the artificial intelligence-based agents to language models to formulate actions operate as tools for analyzing a problem set that can be chained together to address a given workflow, in one or more prompts for constructing and delivering the identified outcomes. Chains of repeatable actions for saved and utilized for additional workflows having similar problem sets, and executed based on pre-identified triggers.